Bquant Spotlight: Asset Allocation in Bquant, A Machine Learning Approach
Machine learning is perfectly positioned to help you take advantage of these data sets—and potentially realize attractive returns.
Join us to learn how a variety of asset class data can be accessed in BQL, and how you can use features in both BQuant and BQuant Enterprise to produce an asset allocation framework using machine learning libraries.
Will we outperform the 60/40 portfolio? Tune in to find out.
Speakers
Aidan Wilmott
Desktop Build Group – Team Leader
Bloomberg L.P.
Aidan looks after a team of quantitative specialists spanning Greater China to Australia and New Zealand. The team primarily looks after the roll out of BQL and BQuant to our buyside customers. Prior to moving to Hong Kong, Aidan was responsible for the UK and Ireland team, and brings extensive experience of EMEA markets.
Christian Contino
BQuant Solution Analyst
Bloomberg L.P.
Christian is the BQuant solution analyst for Australia and New Zealand. He has a wealth of experience in this area, having previously worked in a family office as a quant researcher and at the United Nations as a Fixed Income portfolio manager. He holds a PhD in Financial Statistics from the University of Sydney.